Biotyping in psychosis: using multiple computational approaches with one data set

Carol A. Tamminga, Brett A. Clementz, Godfrey Pearlson, Macheri Keshavan, Elliot S. Gershon, Elena I. Ivleva, Jennifer McDowell, Shashwath A. Meda, Sarah Keedy, Vince D. Calhoun, Paulo Lizano, Jeffrey R. Bishop, Matthew Hudgens-Haney, Ney Alliey-Rodriguez, Huma Asif, Robert Gibbons

Research output: Contribution to journalReview articlepeer-review

21 Scopus citations


Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.

Original languageEnglish (US)
Pages (from-to)143-155
Number of pages13
Issue number1
StatePublished - Sep 26 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to American College of Neuropsychopharmacology.


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